Abstract
BackgroundAgent-based models (ABM) are believed to be a very powerful tool in the social sciences, sometimes even treated as a substitute for social experiments. When building an ABM we have to define the agents and the rules governing the artificial society. Given the complexity and our limited understanding of the human nature, we face the problem of assuming that either personal traits, the situation or both have impact on the social behavior of agents. However, as the long-standing person-situation debate in psychology shows, there is no consensus as to the underlying psychological mechanism and the important question that arises is whether the modeling assumptions we make will have a substantial influence on the simulated behavior of the system as a whole or not.Methodology/Principal FindingsStudying two variants of the same agent-based model of opinion formation, we show that the decision to choose either personal traits or the situation as the primary factor driving social interactions is of critical importance. Using Monte Carlo simulations (for Barabasi-Albert networks) and analytic calculations (for a complete graph) we provide evidence that assuming a person-specific response to social influence at the microscopic level generally leads to a completely different and less realistic aggregate or macroscopic behavior than an assumption of a situation-specific response; a result that has been reported by social psychologists for a range of experimental setups, but has been downplayed or ignored in the opinion dynamics literature.SignificanceThis sensitivity to modeling assumptions has far reaching consequences also beyond opinion dynamics, since agent-based models are becoming a popular tool among economists and policy makers and are often used as substitutes of real social experiments.
Highlights
Agent-based models (ABM) are believed to be a very powerful tool in many disciplines [1,2,3,4,5,6,7,8]
The models used by statistical physicists were rather simple, usually not because the physical reality was simple but because simple models were much easier to deal with and able to describe universal features
Agentbased models that are nowadays used in other disciplines are often more complicated, the seminal model proposed by Thomas Schelling [12] to describe spatial segregation in societies was as simple as the simplest models in statistical physics can get
Summary
Agent-based models (ABM) are believed to be a very powerful tool in many disciplines [1,2,3,4,5,6,7,8]. Agentbased models that are nowadays used in other disciplines are often more complicated, the seminal model proposed by Thomas Schelling [12] to describe spatial segregation in societies was as simple as the simplest models in statistical physics can get It is not the aim of this article to discuss if agent-based models have to be simple or not. In some papers a nonconformist behavior is introduced as an individual trait [22,23], whereas in other as a situational factor [20,24,25,26,27] This raises the question of the role of ABMs. Certainly some of them are just interesting in themselves and can be investigated from the point of view of basic research, in the domain of non-equilibrium statistical physics [9]. These considerations nicely lead us to two questions which have been the motivation for this paper: 1. Micro level: What determines human behavior – personal traits or rather the situation?
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